An incremental model on search engine query recommendation

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• 作者：JianGuo Wang^a ; ^{hellowangjg@126.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Joshua Zhexue Huang^bAuthor Vitae ; Dingming Wu^bAuthor Vitae ; Jiafeng Guo^cAuthor Vitae ; Yanyan Lan^cAuthor Vitae
• 关键词：Query recommendation ; Query log analysis ; Convex optimization ; Incremental update method
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：19 December 2016
• 年：2016
• 卷：218
• 期：Complete
• 页码：423-431
• 全文大小：1120 K

文摘

Search engine query recommendation based on mining query logs has been considered as an important and useful method of facilitating users to retrieve information. However, the log data evolves quickly. Existing query recommendation approaches have to rebuild the models when new log data arrive. In this paper, we extend the query ranking model (QRM) proposed in our previous work (Wang et al., 2015) [1] to an adaptive model in which new coming log data is incrementally added, so that the recommendation model is kept up-to-date. The experimental results have demonstrated that the proposed incremental query ranking model (IQRM) is able to recommend queries more efficiently than re-building QRM on evolving log data without losing accuracy.